Practical ML projects that start with clear business questions and end with models that can be monitored, trusted, and improved over time.
We focus on use‑cases where ML meaningfully improves decisions: forecasting, prioritisation, and pattern detection that would be difficult to capture in rules alone.
We keep the modelling stack lightweight and transparent so your team can operate it after handover.
A finance department needed a more reliable view of expected cash‑in to plan investments and staffing.
Impact: improved forecast accuracy, fewer last‑minute surprises, and more confident planning.
We built scenario sliders so finance could explore optimistic, base, and conservative views without touching the underlying models.
Discuss ML forecasting for your teamA subscription business wanted to focus customer success on accounts most at risk of leaving.
Impact: higher retention in the most at‑risk segments with targeted, proactive outreach.
Scores were surfaced directly in the tools the team already used, so they didn’t need to learn a new interface to act on insights.
Explore churn or propensity models